Leadership Needs to Shift in the Health System: Three Emerging Perspectives to Inform Our Way Forward
Bibliographic record
Abstract
Zimmerman et al. have brought to light a number of issues that lead to a collective failure in healthcare safety culture, and propose how to overcome them. Front-line ownership (FLO) is a great success story in that respect, acknowledging that much of the problem and, therefore, solution, relates to how, not what, approaches and solutions have been implemented. In service of the healthy dialogue the authors have invited, this commentary suggests that there needs to be a purposeful shift in leadership, not only in the important area of patient safety but more generally throughout the health system. Three emerging perspectives around leadership are briefly introduced that provide some insight into FLO's success - complexity leadership, neuroleadership and phronetic leadership. Together, these reflect the importance of the underlying dynamics of how we could (re)frame our approaches to change, engage the right people in the right context and achieve sustainable solutions throughout the health system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.053 | 0.064 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".